WP beginner - How to make money

## packages: remove or add your necessary packages

required_packages <- c("tidyverse", "readxl", "ggthemes", "hrbrthemes", "extrafont", "plotly", "scales", "stringr", "gganimate", "here", "tidytext", "sentimentr", "scales", "DT", "here", "sm", "mblm", "glue", "fs", "knitr", "rmdformats", "janitor", "urltools", "colorspace", "pdftools")

library(ggplot2)    # CRAN v3.3.6
library(colorspace) # CRAN v2.0-3
library(here)       # CRAN v1.0.1
library(dplyr)      # CRAN v1.0.10
library(janitor)    # CRAN v2.1.0
library(gt)         # CRAN v0.5.0
library(tidyr)      # CRAN v1.2.1
library(readr)      # CRAN v2.1.3
library(stringr)    # CRAN v1.4.1
library(tidytext)
library(ggalt)
library(ggchicklet)
library(forcats)
library(ggfittext)
library(patchwork)
library(lubridate)
library(ggforce)

# for(i in required_packages) { 
# if(!require(i, character.only = T)) {
# 
# #  if package is not existing, install then load the package
# install.packages(i, dependencies = T)
# require(i, character.only = T)
# }
# }


## save plots?
save <- TRUE
#save <- FALSE

## quality of png's
dpi <- 750

## font adjust; please adjust to client´s website
#extrafont::loadfonts(device = "win", quiet = TRUE)
#font_add_google("Montserrat", "Montserrat")
# font_add_google("Overpass", "Overpass")
# font_add_google("Overpass Mono", "Overpass Mono")



## theme updates; please adjust to client´s website
#theme_set(ggthemes::theme_clean(base_size = 15))
theme_set(ggthemes::theme_clean(base_size = 20, base_family = "Ubuntu"))


theme_update(plot.margin = margin(30, 30, 30, 30),
             plot.background = element_rect(color = "white",
                                            fill = "white"),
             plot.title = element_text(size = 20,
                                       face = "bold",
                                       lineheight = 1.05,
                                       hjust = .5,color="#ffac36",
                                       margin = margin(10, 0, 25, 0)),
             plot.title.position = "plot",
             plot.caption = element_text(color = "grey40",
                                         size = 9,
                                         margin = margin(20, 0, -20, 0)),
             plot.caption.position = "plot",
             axis.line.x = element_line(color = "black",
                                        size = .8),
             axis.line.y = element_line(color = "black",
                                        size = .8),
             axis.title.x = element_text(size = 19,
                                         face = "bold",
                                         margin = margin(t = 20)),
             axis.title.y = element_text(size = 19,
                                         face = "bold",
                                         margin = margin(r = 20)),
             axis.text = element_text(size = 11,
                                      color = "black",
                                      face = "bold"),
             axis.text.x = element_text(margin = margin(t = 10)),
             axis.text.y = element_text(margin = margin(r = 10)),
             axis.ticks = element_blank(),
             panel.grid.major.x = element_line(size = .6,
                                               color = "#eaeaea",
                                               linetype = "solid"),
             panel.grid.major.y = element_line(size = .6,
                                               color = "#eaeaea",
                                               linetype = "solid"),
             panel.grid.minor.x = element_line(size = .6,
                                               color = "#eaeaea",
                                               linetype = "solid"),
             panel.grid.minor.y = element_blank(),
             panel.spacing.x = unit(4, "lines"),
             panel.spacing.y = unit(2, "lines"),
             legend.position = "top",
             legend.title = element_text(family = "Ubuntu",
                                         size = 14, color="black",
                                         margin = margin(5, 0, 5, 0)),
             legend.text = element_text(family = "Ubuntu",
                                        color = "black",
                                        size = 11,
                                        margin = margin(4.5, 4.5, 4.5, 4.5)),
             legend.background = element_rect(fill = NA,
                                              color = NA),
             legend.key = element_rect(color = NA, fill = NA),
             #legend.key.width = unit(5, "lines"),
             #legend.spacing.x = unit(.05, "pt"),
             #legend.spacing.y = unit(.55, "pt"),
             #legend.margin = margin(0, 0, 10, 0),
             strip.text = element_text(face = "bold",
                                       margin = margin(b = 10)))

## theme settings for flipped plots
theme_flip <-
  theme(panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_line(size = .6,
                                          color = "#eaeaea"))

## theme settings for maps
theme_map <- 
  theme_void(base_family = "Ubuntu") +
  theme(legend.direction = "horizontal",
        legend.box = "horizontal",
        legend.margin = margin(10, 10, 10, 10),
        legend.title = element_text(size = 17, 
                                    face = "bold"),
        legend.text = element_text(color = "grey33",
                                   size = 12),
        plot.margin = margin(15, 5, 15, 5),
        plot.title = element_text(face = "bold",
                                  size = 20,
                                  hjust = .5,
                                  margin = margin(30, 0, 10, 0)),
        plot.subtitle = element_text(face = "bold",
                                     color = "grey33",
                                     size = 17,
                                     hjust = .5,
                                     margin = margin(10, 0, -30, 0)),
        plot.caption = element_text(size = 14,
                                    color = "grey33",
                                    hjust = .97,
                                    margin = margin(-30, 0, 0, 0)))

## numeric format for labels
num_format <- scales::format_format(big.mark = ",", small.mark = ",", scientific = F)

## main color backlinko
bl_col <- "#00d188"
bl_dark <- darken(bl_col, .3, space = "HLS")

## colors + labels for interval stripes
int_cols <- c("#bce2d5", "#79d8b6", bl_col, "#009f66", "#006c45", "#003925")
int_perc <- c("100%", "95%", "75%", "50%", "25%", "5%")

## colors for degrees (Bachelors, Massters, Doctorate in reverse order)
cols_degree <- c("#e64500", "#FFCC00", darken(bl_col, .1))

## gradient colors for position
colfunc <- colorRampPalette(c(bl_col, "#bce2d5"))
pos_cols <- colfunc(10)

Load data

youtube_data <- read_csv(here("proc_data","youtube_data_proc.csv"))
youtube_data_activities <- read_csv(here("proc_data","youtube_data_activities_proc.csv"))
tiktok_data <-  read_csv(here("proc_data","tiktok_data_proc.csv"))
tiktok_data_activities <-  read_csv(here("proc_data","tiktok_data_activities_proc.csv"))

Calculate general stats

yt_vids <- youtube_data %>% distinct(yt_video_id) %>% nrow()
tt_vids <- tiktok_data %>% distinct(tt_video_id) %>% nrow()

youtube_data_activities %>% group_by(yt_video_id) %>% summarise(idn=max(idea)) %>% 
   pull(idn) %>% {length(which(.>1))}-> mult_ideas_yt
tiktok_data_activities %>% group_by(tt_video_id) %>% summarise(idn=max(idea)) %>% 
   pull(idn) %>% {length(which(.>1))} -> mult_ideas_tt

meanytlength <- youtube_data$video_length %>% summary %>% {./60}
meanttlength <- tiktok_data$video_meta_duration %>% summary 

General stats

YouTube: 177 videos (unique video url identifiers, includes YT shorts)
TikTok: 177 videos

YouTube videos are longer (12.15141 minutes on average for the sampled videos), so approximately one third of the videos examined (53/177) included >1 money-making idea. TikTok videos have a shorter maximum length (3 to 10 minutes; 41.14384 seconds on average for the sampled videos) so videos on this platform tend to feature a single idea. Only 4 of the 145 TikTok videos examined provided more than one money-making idea.

Publication dates

youtube_data <- youtube_data %>% mutate(month=month(ymd(youtube_data$publish_date)),
                        pyear=year(ymd(youtube_data$publish_date))) %>% 
  mutate(pub_date=ymd(publish_date))


tiktok_data <- tiktok_data %>% mutate(month=month(ymd_hms(tiktok_data$create_time_iso)),                                pyear=year(ymd_hms(tiktok_data$create_time_iso))) %>%
  mutate(pub_date=date(ymd_hms(create_time_iso))) 

3/4 of the YouTube videos examined were published in 2022, and across all the videos sampled (published since 2018), most are from the summer/fall season (Northern Hemisphere).

TikTok videos in the sample were published between 2019-2022, with more videos uploaded with each passing year. The month with most uploads is July.

tiktok_data %>% tabyl(pyear) %>% round(2)

##  pyear  n percent
##   2019  4    0.03
##   2020 29    0.20
##   2021 50    0.34
##   2022 63    0.43

Publication month also varied between platforms.

youtube_data %>% count(month) %>% 
  ggplot()+
  geom_bar(aes(x=month,y=n),stat = "identity")+
  scale_x_discrete(limits=month.abb) +labs(subtitle = "YouTube data")

tiktok_data %>% count(month) %>% 
  ggplot()+
  geom_bar(aes(x=month,y=n),stat = "identity")+
  scale_x_discrete(limits=month.abb) +labs(subtitle = "TikTok data")

Considering publication dates, videos published earlier do not tend to accumulate more views and comments over time. Engagement is also mostly unrelated to subscriber/follower counts and thus possibly related to content.

ttdatevc <- tiktok_data %>% select(source,pub_date,
                       comments=comment_count,
                       views=play_count,
                       followers=author_meta_fans)
ytdatevc <- youtube_data %>% select(source,pub_date,
                       comments=comments,
                       views=view_count,
                       followers=subs_numeric)
dates_views_comments <- bind_rows(ttdatevc,ytdatevc)

ggplot(dates_views_comments)+
  geom_point(aes(x=pub_date,y=views,color=source))+
  labs(x="Publication date")

ggplot(dates_views_comments)+
  geom_point(aes(x=pub_date,y=comments,color=source))+
    labs(x="Publication date")

ggplot(dates_views_comments)+
  geom_point(aes(x=views,y=comments,color=source))

ggplot(dates_views_comments)+
  geom_point(aes(x=followers,y=comments,color=source))

dates_views_comments %>% filter(followers!=44100000) %>% 
ggplot()+
  geom_point(aes(x=followers,y=comments,color=source))+
  labs(subtitle = "removed outlier")

ggplot(dates_views_comments)+
  geom_point(aes(x=followers,y=views,color=source))

dates_views_comments %>% filter(followers!=44100000) %>% 
ggplot()+
  geom_point(aes(x=followers,y=views,color=source))+
  labs(subtitle = "removed outlier")

Presenter demographics

yt_presenter_demog_gend <- youtube_data %>% tabyl(presenter_gender) %>% 
  mutate(valid_percent=round(valid_percent,2)) %>% mutate(source="YouTube")

yt_malepct <- yt_presenter_demog_gend$valid_percent[2] 
tt_presenter_demog_gend <- tiktok_data %>% tabyl(presenter_gender) %>% 
  mutate(valid_percent=round(valid_percent,2)) %>% mutate(source="TikTok")
tt_malepct <- tt_presenter_demog_gend$valid_percent[2] 
yt_ages <- youtube_data %>% tabyl(presenter_age) %>% na.omit() %>% select(-percent) %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent) %>% mutate(source="YouTube")
tt_ages <- tiktok_data %>% tabyl(presenter_age) %>% na.omit() %>% select(-percent) %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent)%>% mutate(source="TikTok")

Male presenters were represented more on both platforms (YouTube: 0.86% and TikTok 0.8%), and the 20-30 y.o. age category had the highest proportion with ~40%.

gt(yt_ages) %>% tab_header("YouTube")

YouTube
presenter_age	n	percent	source
10 - 20	6	0.03	YouTube
20 - 30	76	0.43	YouTube
30 - 40	49	0.28	YouTube
40 - 50	4	0.02	YouTube
50+	3	0.02	YouTube
Voice-over	20	0.11	YouTube
Voice-over Text-to-Speech	18	0.10	YouTube

gt(tt_ages) %>% tab_header("TikTok")

TikTok
presenter_age	n	percent	source
10 - 20	14	0.10	TikTok
20 - 30	57	0.40	TikTok
30 - 40	24	0.17	TikTok
40 - 50	5	0.04	TikTok
50+	2	0.01	TikTok
Music	8	0.06	TikTok
Voice-over	6	0.04	TikTok
Voice-over Text-to-Speech	25	0.18	TikTok

gendplt <- 
bind_rows(tt_presenter_demog_gend,yt_presenter_demog_gend) %>% na.omit() %>%
  ggplot(aes(x=source,y=valid_percent,fill=presenter_gender,label=presenter_gender))+
  geom_chicklet()+
  labs(x="Video Source",y="presenter representation")+
  geom_bar_text(family="Ubuntu",fullheight = T,size=20)+
  scale_fill_manual(values=c("#003eaa","#46A5FF"),guide="none")

ageplt <- 
bind_rows(yt_ages,tt_ages) %>% 
  filter(str_detect(presenter_age,"^[0-9]")) %>% 
  ggplot(aes(x=fct_reorder(presenter_age,percent),y=n,fill=source,color=source))+
  geom_chicklet(position = "dodge")+
  labs(y="Number of videos",x="Presenter Age",
       caption = "Excludes text-to-speech or voice-over presenters")+
  scale_fill_manual(values = c("#00f2ea","#FF0000"),name="Video Source")+
  scale_color_manual(values=c("#ff0050","#282828"),name="Video Source")+
  coord_flip()

ageplt+gendplt

Categories

YouTube videos, as categorized by their authors, varied in assignment despite the similar overarching topic.

The most common category was Education, followed by How-to % Stlye, and then all the others.

youtube_data %>% tabyl(category) %>% arrange(-n) %>% 
  mutate(across(where(is.numeric),round,2)) %>% gt() %>% tab_header(title = "YouTube data")

YouTube data
category	n	percent
Education	96	0.54
Howto & Style	46	0.26
People & Blogs	27	0.15
Entertainment	7	0.04
News & Politics	1	0.01

Earnings data

ytearn <- 
youtube_data_activities %>% 
  mutate(earnings_1_unit=earnings/earnings_timeframe_number) %>% 
  group_by(yt_video_id,idea,earnings_timeframe) %>% 
  summarise(earn=mean(earnings_1_unit,na.rm=TRUE)) %>% ungroup() %>% na.omit() %>% 
  filter(earnings_timeframe!="No timeframe provided")
ttearn <- 
  tiktok_data_activities %>% 
  mutate(earnings_1_unit=earnings/earnings_timeframe_number) %>% 
  group_by(tt_video_id,idea,earnings_timeframe) %>% 
  summarise(earn=mean(earnings_1_unit,na.rm=TRUE)) %>% ungroup() %>% na.omit() %>% 
  filter(earnings_timeframe!="No timeframe provided")

# earnings time frames
earn_tf <- bind_rows(ytearn,ttearn) %>% count(earnings_timeframe) %>% arrange(-n)

earnings_by_tf <- 
bind_rows(ytearn,ttearn) %>% group_by(earnings_timeframe) %>% 
  summarize(median_earn=median(earn),
            min_earn=min(earn),max_earn=max(earn),
            sd_earn=sd(earn,na.rm = TRUE)) %>% arrange(-median_earn)

• The most common time frame for earnings was for daily income, followed by months and hours.

gt(earn_tf)

earnings_timeframe	n
Days	67
Months	65
Hours	56
Minutes	20
Weeks	9
Years	5
Per Post	1

• Longer time frames report higher median earnings (per 1 unit of time, not standardized)

gt(earnings_by_tf)%>% 
  fmt_number(
    columns = -earnings_timeframe,  
    decimals = 1,
    use_seps = FALSE
  )

earnings_timeframe	median_earn	min_earn	max_earn	sd_earn
Years	100000.0	500.0	400000.0	155449.2
Months	5000.0	15.0	300000.0	50262.5
Weeks	1050.0	24.0	14000.0	4357.9
Days	500.0	2.6	7000.0	1332.4
Hours	35.5	3.0	487.9	110.4
Per Post	33.3	33.3	33.3	NA
Minutes	5.1	0.1	30.0	6.7

Standardized earnings

temporal_earn <- c("Days","Hours","Minutes","Months","Weeks","Years")

yt_tempearn <- ytearn %>% filter(earnings_timeframe %in% temporal_earn)
tt_tempearn <- ttearn %>% filter(earnings_timeframe %in% temporal_earn)

yt_hourly_earn <- 
yt_tempearn %>% mutate(hourly_earn=case_when(
  earnings_timeframe=="Hours"~earn,
  earnings_timeframe=="Minutes"~earn*60,
  earnings_timeframe=="Days"~earn/8,
  earnings_timeframe=="Weeks"~earn/40,
  earnings_timeframe=="Months"~earn/200,
  earnings_timeframe=="Years"~earn/2400
)) %>% mutate(source="YouTube")

tt_hourly_earn <- 
  tt_tempearn %>% mutate(hourly_earn=case_when(
    earnings_timeframe=="Hours"~earn,
    earnings_timeframe=="Minutes"~earn*60,
    earnings_timeframe=="Days"~earn/8,
    earnings_timeframe=="Weeks"~earn/40,
    earnings_timeframe=="Months"~earn/200,
    earnings_timeframe=="Years"~earn/2400
  )) %>% mutate(source="TikTok")

all_earn <- bind_rows(yt_hourly_earn,tt_hourly_earn) 

tt_h_earnsum <- 
tt_hourly_earn %>% summarize(mean_earn=mean(hourly_earn),
                             median_earn=median(hourly_earn))
yt_h_earnsum <- 
yt_hourly_earn %>% summarize(mean_earn=mean(hourly_earn),
                             median_earn=median(hourly_earn))

hourly_med <- median(all_earn$hourly_earn)


bind_rows(yt_hourly_earn,tt_hourly_earn) %>% 
  ggplot()+
  geom_histogram(aes(hourly_earn,fill=source,color=source),
                 alpha=0.7,linewidth=0.6,bins=25)+
  labs(x="Hourly earnings ($)",
       y="Number of money-making ideas",
       title="Distribution of standardized hourly earnings")+
  scale_fill_manual(values = c("#00f2ea","#FF0000"),name="Video Source")+
  scale_color_manual(values=c("#ff0050","#282828"),name="Video Source")+
  theme(plot.title = element_text(color = "#ffac36"),
        legend.position = "bottom",text = element_text(family='Ubuntu'))

For videos that report earnings associated with a temporal reference ($ earned per unit of time), earnings can be reported in a common unit by assuming 8 hour work days and 5 day work weeks. The median hourly earnings is 50.

Once standardized, the timeframe with the highest median earnings was minutes. For this time frame, 18 of 20 videos suggest that it is possible to make between 1 and 30 USD per minute with their ideas. This becomes up to $1800 per hour.

all_earn %>% group_by(earnings_timeframe) %>% 
  summarize(median_earn=median(hourly_earn),
            min_earn=min(hourly_earn),max_earn=max(hourly_earn),
            sd_earn=sd(hourly_earn,na.rm = TRUE)) %>% arrange(-median_earn) %>% gt() %>% 
  fmt_number(
    columns = -earnings_timeframe,  
    decimals = 1,
    use_seps = FALSE
  )

earnings_timeframe	median_earn	min_earn	max_earn	sd_earn
Minutes	306.0	3.0	1800.0	403.6
Days	62.5	0.3	875.0	166.6
Years	41.7	0.2	166.7	64.8
Hours	35.5	3.0	487.9	110.4
Weeks	26.2	0.6	350.0	108.9
Months	25.0	0.1	1500.0	251.3

Across all videos, earnings are right-skewed. 90% of videos report hourly earnings < 384.138.

std hourly earnings varied by platform

This distribution is also evident within earnings timeframes.

bind_rows(yt_hourly_earn,tt_hourly_earn) %>% 
  ggplot()+
  geom_histogram(aes(earn))+
  facet_wrap(~earnings_timeframe,scales = 'free')

Earnings by category (YouTube)

The more common categories (Education, Howto & Style) did not report the higher mean or median standardized earnings. Instead, the People and Blogs category and Entertainment had the top two positions.

yt_hourlycorrs <- left_join(yt_hourly_earn,youtube_data_activities)

yt_hourlycorrs_chp <- yt_hourlycorrs  %>% group_by(yt_video_id,idea) %>%
  chop(business_type_level_1) %>% chop(business_type_level_2) %>% chop(skills_required) %>% ungroup()

yt_hourlycorrs_chp %>% 
  group_by(category) %>% summarise(mean_earn=mean(hourly_earn),
                                   med_earn=median(hourly_earn)) %>% 
  arrange(-mean_earn) %>% gt()

category	mean_earn	med_earn
People & Blogs	386.75750	168.75
Entertainment	127.05000	125.00
Howto & Style	105.40231	80.00
Education	98.01758	40.00

yt_hourlycorrs_chp %>% 
  ggplot(aes(x=category,y=hourly_earn,color=category))+
  geom_sina() + scale_color_discrete(guide="none")

Business types and activities

YouTube - Level 1

For all YouTube videos, the predominant Business Type for the money-making ideas was Publication, Media, and Blogs, followed by the Service Business. Other business types were less common.

# without earnings
yt_acts_chp <- youtube_data_activities  %>% group_by(yt_video_id,idea) %>%
  chop(business_type_level_1) %>% chop(business_type_level_2) %>% chop(skills_required) %>% ungroup()

yt_bus1 <- youtube_data_activities  %>% group_by(yt_video_id,idea) %>%
  unchop(business_type_level_1) %>%  ungroup()

bus1ct <- yt_bus1 %>% group_by(yt_video_id,idea) %>% 
  distinct(yt_video_id,idea,business_type_level_1) %>% 
  tabyl(business_type_level_1) %>% arrange(-n)

bus1ct %>% na.omit() %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent) %>% 
  gt() %>% tab_header("YouTube",subtitle = "Business Types, all videos") 

YouTube
Business Types, all videos
business_type_level_1	n	percent
Publication, Media & Blog	175	0.45
Service Business	122	0.31
Ecommerce & Consumer	56	0.14
Investing	27	0.07
Software & Tech	9	0.02

For videos and ideas with reported earnings, the business activity with the highest earnings (standardized) was Publication, Media, & Blog, followed by investing.

# with earninings

yt_hourlycorrs_bus1 <- yt_hourlycorrs  %>% group_by(yt_video_id,idea) %>%
unchop(business_type_level_1) %>%  ungroup()

yt_hourlycorrs_bus1 %>% group_by(yt_video_id,idea) %>% 
  distinct(yt_video_id,idea,business_type_level_1,hourly_earn) %>% ungroup() %>% 
  group_by(business_type_level_1) %>% 
  summarise(mean_earn=mean(hourly_earn),
            median_earn=median(hourly_earn)) %>% 
  arrange(-median_earn) %>% gt() %>% tab_header("YouTube",
                                                subtitle = "standardized hourly earning by business types")

YouTube
standardized hourly earning by business types
business_type_level_1	mean_earn	median_earn
Publication, Media & Blog	163.73000	70.00000
Investing	31.20833	26.25000
Service Business	76.22654	25.00000
Software & Tech	20.00000	20.00000
Ecommerce & Consumer	48.28748	16.77083

However, there is considerable variation in earnings across the different business types

yt_hourlycorrs_bus1 %>% group_by(yt_video_id,idea) %>% 
  distinct(yt_video_id,idea,business_type_level_1,hourly_earn) %>% ungroup %>% 
  ggplot()+
  geom_sina(aes(x=str_wrap(business_type_level_1,12),
                  y=hourly_earn,color=business_type_level_1))+
  scale_color_discrete(guide="none")+labs(x="Business Type (level 1)")

YouTube - Level 2

yt_bus2 <- youtube_data_activities  %>% group_by(yt_video_id,idea) %>%
  unchop(business_type_level_2) %>%  ungroup()
n_bus2 <- youtube_data_activities %>% distinct(business_type_level_2) %>% nrow()
maxnbus2 <- yt_bus2 %>% group_by(yt_video_id,idea) %>% 
    distinct(yt_video_id,idea,business_type_level_2) %>% ungroup() %>% group_by(yt_video_id,idea) %>% summarise(nbus2=n()) %>% arrange(-nbus2) %>% pull(nbus2) %>% max()

The second-level classification of Business Activities for making money includes many more categories (51). Many combinations of Business Types were possible for each video/idea, but none included more than 3.

At this level no particular business type predominated, none represented >20% of suggested activities. The most frequent business type was Publication, Media & Blog - Affiliate Marketing, followed by Service Business - Microwork/tasks (e.g. Taking surveys, searching for data, tagging content and identifying images). Other types were much less common.

bus2ct <- yt_bus2 %>% group_by(yt_video_id,idea) %>% 
  distinct(yt_video_id,idea,business_type_level_2) %>% 
  tabyl(business_type_level_2) %>% arrange(-n)

bus2ct %>% na.omit() %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent) %>% filter(n>1) %>% 
  gt() %>% tab_header("YouTube",subtitle = "Business Types (LEVEL 2), all videos") %>% tab_footnote(footnote = "n=1 not shown",
                           locations = cells_column_labels(
                             columns = n
                           )) 

YouTube
Business Types (LEVEL 2), all videos
business_type_level_2	n1	percent
Publication, Media & Blog - Affiliate Marketing	86	0.19
Service Business - Microwork/tasks (e.g. Taking surveys, searching for data, tagging content and identifying images)	64	0.14
Publication, Media & Blog - YouTube	35	0.08
Service Business - Other freelance (e.g. on Upwork, Fiverr)	25	0.06
Service Business - Agency (e.g. Social Media Marketing, SEO, etc.)	19	0.04
Ecommerce & Consumer - Online shop on marketplaces (e.g. Ebay, Etsy)	18	0.04
Publication, Media & Blog - Courses	17	0.04
Ecommerce & Consumer - Dropshipping	14	0.03
Ecommerce & Consumer - Online Shop	14	0.03
Investing - Crypto	13	0.03
Publication, Media & Blog - Write a blog	11	0.02
Ecommerce & Consumer - Amazon FBA	10	0.02
Investing - Stocks	10	0.02
Ecommerce & Consumer - Sell belongings on marketplaces (e.g. Ebay, Craigslist, Etsy)	9	0.02
Investing - Real estate investing (e.g. House flipping/ Crowdfunding)	8	0.02
Publication, Media & Blog - Selling Digital Products (EBooks/Print On Demand etc))	8	0.02
Publication, Media & Blog - Content Creator	7	0.02
Publication, Media & Blog - Influencer	5	0.01
Publication, Media & Blog - Youtube/Rumble/Pinterest Video Automation	5	0.01
Service Business - Become a Virtual Assistant	5	0.01
Software & Tech - Create A Website	5	0.01
Service Business - Freelance Writer	4	0.01
Ecommerce & Consumer - Facebook Marketplace	3	0.01
Publication, Media & Blog - Paid Community (Patreon)	3	0.01
Publication, Media & Blog - Spinning Articles	3	0.01
Publication, Media & Blog - Youtube Sponsorships	3	0.01
Service Business - Rent out stuff (e.g. storage space, truck)	3	0.01
Publication, Media & Blog - Image Sharing	2	0.00
Publication, Media & Blog - Membership Sites	2	0.00
Publication, Media & Blog - Newsletter/ Articles	2	0.00
Publication, Media & Blog - NFT's	2	0.00
Publication, Media & Blog - Podcasting	2	0.00
Publication, Media & Blog - Write a book	2	0.00
Service Business - Home Delivery Services	2	0.00
Service Business - Home Services (e.g. Power Washing, Pet sitting)	2	0.00
Service Business - Video Editor	2	0.00
Software & Tech - Create a mobile app	2	0.00
Software & Tech - Create a software	2	0.00
Software & Tech - Create Templates	2	0.00
1 n=1 not shown

The Business type with the highest mean standardized earnings was Publication, Media & Blog - Newsletter/ Articles, followed by Ecommerce & Consumer - Dropshipping and other types in the Publication/Media/Blogging fields.

yt_hourlycorrs_bus2 <- yt_hourlycorrs  %>% group_by(yt_video_id,idea) %>%
unchop(business_type_level_2) %>%  ungroup()
yt_hourlycorrs_bus2 %>% group_by(yt_video_id,idea) %>% 
  distinct(yt_video_id,idea,business_type_level_2,hourly_earn) %>% ungroup() %>% 
  group_by(business_type_level_2) %>% 
  summarise(mean_earn=mean(hourly_earn),
            median_earn=median(hourly_earn)) %>% 
  arrange(-median_earn) %>% gt() %>% tab_header("YouTube, Business Type Level 2",
                                                subtitle = "standardized hourly earning by business types")

YouTube, Business Type Level 2
standardized hourly earning by business types
business_type_level_2	mean_earn	median_earn
Publication, Media & Blog - Write a blog	925.00000	925.00000
Publication, Media & Blog - Newsletter/ Articles	312.50000	312.50000
Ecommerce & Consumer - Dropshipping	255.56820	255.56820
Publication, Media & Blog - Youtube Sponsorships	230.62500	230.62500
Publication, Media & Blog - Spinning Articles	718.33333	180.00000
Service Business - Sales Representative/Advisor	170.00000	170.00000
Publication, Media & Blog - Affiliate Marketing	179.31921	105.65000
Publication, Media & Blog - Youtube/Rumble/Pinterest Video Automation	105.00000	105.00000
Publication, Media & Blog - YouTube	118.58227	75.00000
Publication, Media & Blog - Membership Sites	70.00000	70.00000
Service Business - Rent out stuff (e.g. storage space, truck)	68.75000	68.75000
Investing - Crypto	62.50000	62.50000
Publication, Media & Blog - Selling Digital Products (EBooks/Print On Demand etc))	58.42406	56.25000
Publication, Media & Blog - Content Creator	50.00000	50.00000
Software & Tech - Create A Website	47.50000	47.50000
Service Business - Home Services (e.g. Power Washing, Pet sitting)	45.62500	45.62500
Service Business - Other freelance (e.g. on Upwork, Fiverr)	264.55625	40.62500
Service Business - Agency (e.g. Social Media Marketing, SEO, etc.)	47.08333	37.50000
Service Business - Become a Virtual Assistant	40.62500	35.00000
Investing - Real estate investing (e.g. House flipping/ Crowdfunding)	31.35417	31.35417
Service Business - Freelance Writer	40.50000	30.00000
Service Business - Microwork/tasks (e.g. Taking surveys, searching for data, tagging content and identifying images)	113.01125	28.50000
Publication, Media & Blog - NFT's	26.25000	26.25000
Publication, Media & Blog - Creating Spotify Ads	25.00000	25.00000
Service Business - Home Delivery Services	22.41667	22.41667
Publication, Media & Blog - Courses	77.02381	20.00000
Ecommerce & Consumer - Online shop on marketplaces (e.g. Ebay, Etsy)	20.84028	16.77083
Service Business - Video Editor	15.00000	15.00000
Publication, Media & Blog - Paid Community (Patreon)	27.30518	10.00000
Service Business - Data Entry	10.00000	10.00000
Ecommerce & Consumer - Online Shop	9.09500	9.09500
Investing - Stocks	22.33333	4.00000

The five business types at this level with n>6 show a wide range of earnings.

yt_hourlycorrs_bus2 %>% group_by(yt_video_id,idea) %>% 
  distinct(yt_video_id,idea,business_type_level_2,hourly_earn) %>% ungroup %>% add_count(business_type_level_2) %>% filter(n>6) %>% 
  ggplot()+
  geom_sina(aes(x=str_wrap(business_type_level_2,33),
                  y=hourly_earn,color=business_type_level_2))+
  scale_color_discrete(guide="none")+labs(x="Business Type (level 1)")+
  coord_flip()+labs(x="Business Type Level 2")

TikTok - Level 1

For TikTok videos, the predominant Business Type for the money-making ideas was Service Business with almost 50% of videos, followed by the Ecommerce & Consumer ventures. Other business types were less common.

# tt without earnings
tt_acts_chp <- tiktok_data_activities  %>% group_by(tt_video_id,idea) %>%
  chop(business_type_level_1) %>% chop(business_type_level_2) %>% chop(skills_required) %>% ungroup()

tt_bus1 <- tiktok_data_activities  %>% group_by(tt_video_id,idea) %>%
  unchop(business_type_level_1) %>%  ungroup()

tt_bus1ct <- tt_bus1 %>% group_by(tt_video_id,idea) %>% 
  distinct(tt_video_id,idea,business_type_level_1)  %>% 
  tabyl(business_type_level_1) %>% arrange(-n)


tt_bus1ct %>% na.omit() %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent) %>% 
  gt() %>% tab_header("TikTok",subtitle = "Business Types, all videos") 

TikTok
Business Types, all videos
business_type_level_1	n	percent
Service Business	74	0.49
Ecommerce & Consumer	36	0.24
Publication, Media & Blog	28	0.19
Investing	12	0.08
Software & Tech	1	0.01

For videos and ideas with reported earnings, the business activity with the highest earnings (standardized) was Investing, followed by Ecommerce & Consumer

# tt with earninings
tt_hourlycorrs <- left_join(tt_hourly_earn,tiktok_data_activities)

tt_hourlycorrs_chp <- tt_hourlycorrs  %>% group_by(tt_video_id,idea) %>%
  chop(business_type_level_1) %>% chop(business_type_level_2) %>% chop(skills_required) %>% ungroup()

tt_hourlycorrs_bus1 <- tt_hourlycorrs  %>% group_by(tt_video_id,idea) %>%
  unchop(business_type_level_1) %>%  ungroup()

tt_hourlycorrs_bus1 %>% group_by(tt_video_id,idea) %>% 
  distinct(tt_video_id,idea,business_type_level_1,hourly_earn) %>% ungroup() %>% 
  group_by(business_type_level_1) %>% 
  summarise(mean_earn=mean(hourly_earn),
            median_earn=median(hourly_earn)) %>% 
  arrange(-median_earn) %>% gt()

business_type_level_1	mean_earn	median_earn
Ecommerce & Consumer	227.55849	134.90062
Investing	82.22282	56.11292
Service Business	102.44798	40.00000
Publication, Media & Blog	261.69231	37.50000
Software & Tech	1.48500	1.48500

However, with some exceptions, earnings do not vary considerably across the different business types

tt_hourlycorrs_bus1 %>% group_by(tt_video_id,idea) %>% 
  distinct(tt_video_id,idea,business_type_level_1,hourly_earn) %>% ungroup %>% 
  ggplot()+
  geom_jitter(aes(x=str_wrap(business_type_level_1,12),
                  y=hourly_earn,color=business_type_level_1))+
  scale_color_discrete(guide="none")+labs(x="Business Type (level 1)",subtitle = "TikTok")

# 1 saving plots in pdf with example

# ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color= drv)) + 
#      geom_smooth(mapping = aes(linetype = drv), method = 'loess') +
#      geom_point()
# 
# if(save == T){ 
#   ggsave(here::here("plots", "name_plot.pdf"), 
#          width = 12.5, height = 8, device = cairo_pdf)
# }

# 2 pdfs will then be converted into the pngs using the 04_convert_pdfs_to_pngs.rmd file. 

View counts, comments, followers, and standardized earnings are not tightly associated.

yt_hourly_renamed <-  yt_hourlycorrs_chp %>%  
  select(earn=hourly_earn,views=view_count,source,comments,followers=subs_numeric)
  
tt_hourly_renamed <-  tt_hourlycorrs_chp %>%  
  select(earn=hourly_earn,views=play_count,source,comments=comment_count,
           followers=author_meta_fans)

hourlyboth <- bind_rows(yt_hourly_renamed,tt_hourly_renamed)

ggplot(hourlyboth)+aes(x=views,y=earn,color=source)+geom_point()

ggplot(hourlyboth)+aes(x=comments,y=earn,color=source)+geom_point()

ggplot(hourlyboth)+aes(x=followers,y=earn,color=source)+geom_point()

TikTok - Level 2

n_bus2tt <- tiktok_data_activities %>% distinct(business_type_level_2) %>% nrow()

tt_bus2 <- tiktok_data_activities  %>% group_by(tt_video_id,idea) %>%
  unchop(business_type_level_2) %>%  ungroup()
maxnbus2tt <- tt_bus2 %>% group_by(tt_video_id,idea) %>% 
    distinct(tt_video_id,idea,business_type_level_2) %>% ungroup() %>% group_by(tt_video_id,idea) %>% summarise(nbus2=n()) %>% arrange(-nbus2) %>% pull(nbus2) %>% max()

TikTok videos mentioned fewer (33) Business Activities in this level than Youtube videos. Many combinations of Business Types were possible for each video/idea, but none included more than 3.

No particular business type predominated, none represented >20% of suggested activities. The most frequent business type was Service Business - Microwork/tasks (e.g. Taking surveys, searching for data, tagging content and identifying images), followed by Ecommerce & Consumer - Online shop on marketplaces (e.g. Ebay, Etsy). Other types were much less common.

bus2ctt <- tt_bus2 %>% group_by(tt_video_id,idea) %>% 
  distinct(tt_video_id,idea,business_type_level_2) %>% 
  tabyl(business_type_level_2) %>% arrange(-n)

bus2ctt %>% na.omit() %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent) %>% filter(n>1) %>% 
  gt() %>% tab_header("TikTok",subtitle = "Business Types (LEVEL 2), all videos") %>% tab_footnote(footnote = "n=1 not shown",
                           locations = cells_column_labels(
                             columns = n
                           )) 

TikTok
Business Types (LEVEL 2), all videos
business_type_level_2	n1	percent
Service Business - Microwork/tasks (e.g. Taking surveys, searching for data, tagging content and identifying images)	30	0.16
Ecommerce & Consumer - Online shop on marketplaces (e.g. Ebay, Etsy)	16	0.09
Ecommerce & Consumer - Dropshipping	14	0.08
Service Business - Agency (e.g. Social Media Marketing, SEO, etc.)	10	0.05
Publication, Media & Blog - Affiliate Marketing	9	0.05
Service Business - Other freelance (e.g. on Upwork, Fiverr)	9	0.05
Ecommerce & Consumer - Amazon FBA	8	0.04
Investing - Real estate investing (e.g. House flipping/ Crowdfunding)	8	0.04
Ecommerce & Consumer - Online Shop	7	0.04
Publication, Media & Blog - Selling Digital Products (EBooks/Print On Demand etc))	7	0.04
Publication, Media & Blog - YouTube	6	0.03
Service Business - Sales Representative/Advisor	5	0.03
Service Business - Vending Machine	5	0.03
Ecommerce & Consumer - Facebook Marketplace	4	0.02
Publication, Media & Blog - Image Sharing	4	0.02
Publication, Media & Blog - Youtube/Rumble/Pinterest Video Automation	4	0.02
Service Business - Become a Virtual Assistant	4	0.02
Service Business - Home Delivery Services	4	0.02
Service Business - Home Services (e.g. Power Washing, Pet sitting)	4	0.02
Ecommerce & Consumer - Sell belongings on marketplaces (e.g. Ebay, Craigslist, Etsy)	3	0.02
Investing - Stocks	3	0.02
Publication, Media & Blog - NFT's	3	0.02
Service Business - Freelance Writer	3	0.02
Service Business - Rent out room (e.g. Airbnb)	3	0.02
Service Business - Car Wash	2	0.01
Service Business - Furniture Flipping	2	0.01
Service Business - Rent out stuff (e.g. storage space, truck)	2	0.01
1 n=1 not shown

The Business type with the highest mean standardized earnings was Publication, Media & Blog - Write a blog (although this result is driven by a single video stating $300000 in monthly earnings through this method). After that Service Business - Furniture Flipping and Investing - Stocks have similar earnings, followed by various others.

tt_hourlycorrs_bus2 <- tt_hourlycorrs  %>% group_by(tt_video_id,idea) %>%
unchop(business_type_level_2) %>%  ungroup()

tt_hourlycorrs_bus2 %>% group_by(tt_video_id,idea) %>% 
  distinct(tt_video_id,idea,business_type_level_2,hourly_earn) %>% ungroup() %>% 
  group_by(business_type_level_2) %>% 
  summarise(mean_earn=mean(hourly_earn),
            median_earn=median(hourly_earn)) %>% 
  arrange(-median_earn) %>% gt() %>% tab_header("TikTok, Business Type Level 2",
                                                subtitle = "standardized hourly earning by business types")

TikTok, Business Type Level 2
standardized hourly earning by business types
business_type_level_2	mean_earn	median_earn
Publication, Media & Blog - Write a blog	1500.0000000	1500.0000000
Ecommerce & Consumer - Online shop on marketplaces (e.g. Ebay, Etsy)	245.8878889	150.0000000
Service Business - Furniture Flipping	140.6250000	140.6250000
Investing - Real estate investing (e.g. House flipping/ Crowdfunding)	135.0000000	135.0000000
Ecommerce & Consumer - Amazon FBA	195.7437500	119.8012500
Ecommerce & Consumer - Online Shop	100.0000000	100.0000000
Service Business - Agency (e.g. Social Media Marketing, SEO, etc.)	73.8194444	75.0000000
Service Business - Other freelance (e.g. on Upwork, Fiverr)	79.8809524	75.0000000
Service Business - Home Delivery Services	63.4843750	63.4843750
Service Business - Microwork/tasks (e.g. Taking surveys, searching for data, tagging content and identifying images)	160.4275000	61.5000000
Investing - Stocks	55.8342361	55.8342361
Ecommerce & Consumer - Dropshipping	184.4657571	50.0000000
Service Business - Car Wash	50.0000000	50.0000000
Service Business - Home Services (e.g. Power Washing, Pet sitting)	45.0000000	40.0000000
Publication, Media & Blog - Affiliate Marketing	326.0000000	37.5000000
Publication, Media & Blog - YouTube	501.5000000	37.5000000
Publication, Media & Blog - Youtube/Rumble/Pinterest Video Automation	323.3333333	37.5000000
Service Business - Freelance Writer	68.0555556	30.0000000
Publication, Media & Blog - Selling Digital Products (EBooks/Print On Demand etc))	45.8333333	25.0000000
Service Business - Become a Virtual Assistant	29.9950000	21.9850000
Service Business - Rent out room (e.g. Airbnb)	18.3175000	18.3175000
Service Business - Sales Representative/Advisor	18.7400000	18.0000000
Ecommerce & Consumer - Sell belongings on marketplaces (e.g. Ebay, Craigslist, Etsy)	9.1636667	9.1636667
Software & Tech - Create a software	1.4850000	1.4850000
Service Business - Vending Machine	0.6703125	0.6703125

The six business types at this level with n>5 also show a wide range of earnings.

tt_hourlycorrs_bus2 %>% group_by(tt_video_id,idea) %>% 
  distinct(tt_video_id,idea,business_type_level_2,hourly_earn) %>% ungroup %>% add_count(business_type_level_2) %>% filter(n>5) %>% 
  ggplot()+
  geom_sina(aes(x=str_wrap(business_type_level_2,33),
                  y=hourly_earn,color=business_type_level_2))+
  scale_color_discrete(guide="none")+labs(x="Business Type (level 1)")+
  coord_flip()+labs(x="Business Type Level 2")

Share of business types (Level 1) by platform

bus1ct <- bus1ct %>% na.omit %>% mutate(source="YouTube")
tt_bus1ct <- tt_bus1ct %>% na.omit %>% mutate(source="TikTok")

bind_rows(bus1ct,tt_bus1ct) %>% 
ggplot()+
  geom_bar(aes(x=fct_reorder(business_type_level_1,n),y=n,fill=source,
               color=source),stat="identity",linewidth=0.7,
           position = "dodge")+
  labs(x="Business Type",
       y="Number of money-making activities",
       title="Popular Business Types by Platform")+
  scale_fill_manual(values = c("#00f2ea","#FF0000"),name="Video Source")+
  scale_color_manual(values=c("#ff0050","#282828"),name="Video Source")+
  theme(plot.title = element_text(color = "#ffac36"),
        legend.position = "bottom",text = element_text(family='Ubuntu'))+
  coord_flip()

Earnings per business type

ytbus1earn <- 
yt_hourlycorrs_bus1 %>% group_by(yt_video_id,idea) %>% 
  distinct(yt_video_id,idea,business_type_level_1,hourly_earn) %>% ungroup %>% mutate(source="YouTube") 

ttbus1earn <- 
tt_hourlycorrs_bus1 %>% group_by(tt_video_id,idea) %>% 
  distinct(tt_video_id,idea,business_type_level_1,hourly_earn) %>% ungroup %>% mutate(source="TikTok") 

bind_rows(ytbus1earn,ttbus1earn) %>% add_count(business_type_level_1) %>% 
ggplot()+
  geom_sina(aes(x=fct_reorder(business_type_level_1,n),
                  y=hourly_earn,fill=business_type_level_1, ),color="black",pch=21,size=3)+
  scale_fill_manual(values= c("#0060df","#003eaa","#46B100","#46A5FF","#F3B71F"), guide="none")+labs(x="Business Type",
                                          y="Hourly earnings (USD)")+
  coord_flip()+theme(text = element_text(size=25))

Top earners

On YouTube, these three videos (from three different creators) report the highest earnings.

yt_hourlycorrs_chp %>% slice_max(hourly_earn,n = 3) %>% select(video_url,title,author,earnings_timeframe,earn,hourly_earn) %>% gt()

video_url	title	author	earnings_timeframe	earn	hourly_earn
https://www.youtube.com/watch?v=h6C0Dq_wcJ0	FREE Bot Pays You $30.00 Per Minute in Passive Income [Make Money Online]	Online Hustle	Minutes	30	1800
https://www.youtube.com/watch?v=1_XD-J0u5E8	NEW $7000/Day Copy Paste Website Pays Beginners! (Make Money Online)	Online Hustle	Days	7000	875
https://www.youtube.com/watch?v=YDZ3M0Az8BU	7 Ways To Make Your First $100,000 Online	Iman Gadzhi	Months	150000	750

On TikTok, these three videos (from three different creators) report the highest earnings.

tt_hourlycorrs_chp %>% slice_max(hourly_earn,n = 3) %>% select(web_video_url,text,author_meta_nick_name,earnings_timeframe,earn,hourly_earn) %>% gt()

web_video_url	text	author_meta_nick_name	earnings_timeframe	earn	hourly_earn
https://www.tiktok.com/@adamenfroy/video/7132196947120753963	The most underrated #sidehustle? #makemoneyonline #blogging #affiliatemarketing	adamenfroy	Months	300000	1500.0
https://www.tiktok.com/@lukeisshorts/video/7119011507559664942	Anybody can do it #investor #passiveincome #makemoneyfromhome	Luke Robins	Months	184500	922.5
https://www.tiktok.com/@ecomjoshcarter/video/6924254455114370305	I QUIT MY 9-5... #ebay #dropshipping #ecom #makemoney	ecomjoshcarter	Days	7000	875.0

In particular, author “Adam Enfroy” appears on both of these platforms reporting 100-300k USD in monthly earnings.

Bottom earners

yt_hourlycorrs_chp %>% slice_min(hourly_earn,n = 3) %>% select(video_url,title,author,earnings_timeframe,earn,hourly_earn) %>% gt()

video_url	title	author	earnings_timeframe	earn	hourly_earn
https://www.youtube.com/watch?v=WGxKjgy7R4g	Stupid App Gives $500 To Beginners Who DO NOTHING [Make Money Online]	Online Hustle	Months	15	0.0750000
https://www.youtube.com/watch?v=_bUsIgEFTTc	7.5 Passive Income Ideas To Easily Make $500/Day	Vincent Chan	Years	500	0.2083333
https://www.youtube.com/watch?v=N7LFPNYgMOI	Best 10 Apps That Pay You Real Money | Make Money Online	Mr Reis	Months	55	0.2750000

tt_hourlycorrs_chp %>% slice_min(hourly_earn,n = 3) %>% select(web_video_url,text,author_meta_nick_name,earnings_timeframe,earn,hourly_earn) %>% gt()

web_video_url	text	author_meta_nick_name	earnings_timeframe	earn	hourly_earn
https://www.tiktok.com/@pristinevending/video/7112240282468388138	How much did this machine make after 2 months? #vendingmachinebusiness #vendingmachine #sidehustle #sidehustleideas #drinkmachine	PristineVending	Months	18.375000	0.0918750
https://www.tiktok.com/@ebrahim_ka/video/6875602063842086146	5 ways to make money at home #howtomakemoney #makemoney #moneyathome #dubai	Ebrahim	Months	52.000000	0.2600000
https://www.tiktok.com/@mattlorion/video/6840985541836623109	Turning $0 into $100,000 (Part 1) #fyp #entrepreneur #makemoney #flipping	Matt Lorion	Days	2.618667	0.3273333

Skills

Different money-making ideas on both platforms varied in the number of skills needed to generate earnings. For the most part, each idea needed only one or two different skills, and this was more evident on TikTok (much shorter videos with generally only one money-making idea).

skills_per_idea_yt <- 
youtube_data_activities %>% select(yt_video_id,idea,skills_required) %>% 
  group_by(yt_video_id,idea) %>%  distinct() %>% 
summarise(n_skills=n()) %>% ungroup() %>% mutate(source="YouTube") %>% 
    select(n_skills,source)

skills_per_idea_tt <- 
tiktok_data_activities %>% select(tt_video_id,idea,skills_required) %>% 
  group_by(tt_video_id,idea) %>%  distinct() %>% 
summarise(n_skills=n()) %>% ungroup()%>% mutate(source="TikTok") %>% 
  select(n_skills,source)

bind_rows(skills_per_idea_tt,skills_per_idea_yt) %>% 
ggplot()+
  geom_histogram(aes(x=n_skills,fill=source))

The makeup of required skills also varied across platforms.

On YouTube, the most mentioned skill was Marketing followed by Image or Video Editing.

youtube_data_activities %>% select(yt_video_id,idea,skills_required) %>% distinct() %>% tabyl(skills_required) %>% arrange(-n) %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent) %>% na.omit %>% gt() %>% tab_header(title = "YouTube")

YouTube
skills_required	n	percent
Marketing	152	0.24
Image Editing	83	0.13
Video Editing	81	0.13
Writing	70	0.11
Investing	69	0.11
Doing mircowork for businesses	67	0.11
Speaking	54	0.09
Web Development	29	0.05
Programming	22	0.03
Providing Home Services	3	0.00
Gaming	2	0.00

When an idea or video required two or more skills, the most common combinations were Speaking + Video Editing, Investing + Marketing, Speaking + Writing, and Image + Video Editing.

youtube_data_activities %>% select(yt_video_id,idea,skills_required) %>% 
  group_by(yt_video_id,idea) %>% 
  arrange(yt_video_id, skills_required) %>% 
  summarize(combination = paste0(skills_required, collapse = " - "), .groups = "drop") %>% 
  count(combination) %>% 
  filter(str_detect(combination," - ")) %>%  arrange(-n) %>% slice(1:10) %>% 
  gt() %>% tab_header("YouTube Data", subtitle="Combinations of >2 skills, Top 10 most common combinations shown")

YouTube Data
Combinations of >2 skills, Top 10 most common combinations shown
combination	n
Speaking - Video Editing	13
Investing - Marketing	12
Speaking - Writing	12
Image Editing - Video Editing	7
Image Editing - Marketing	6
Image Editing - Marketing - Programming - Video Editing - Web Development - Writing	6
Image Editing - Investing	5
Image Editing - Speaking - Video Editing	4
Image Editing - Speaking - Video Editing - Writing	4
Investing - Investing	4

TikTok videos favored Investing, followed by Marketing and Writing.

tiktok_data_activities %>% group_by(tt_video_id,idea) %>% tabyl(skills_required) %>% arrange(-n) %>% mutate(percent=round(valid_percent,2)) %>% select(-valid_percent) %>% na.omit %>% gt() %>% tab_header(title = "TikTok")

TikTok
skills_required	n	percent
Investing	73	0.26
Marketing	57	0.20
Writing	37	0.13
Image Editing	33	0.12
Doing mircowork for businesses	32	0.11
Video Editing	21	0.07
Providing Home Services	9	0.03
Speaking	8	0.03
Web Development	8	0.03
Programming	5	0.02
Gaming	1	0.00

Despite the fewer videos that required >1 skill, there were some frequently mentioned skill combinations such as Investing + Marketing, followed by Marketing+Programming+Video Editing+Web Development+Writing.

tiktok_data_activities %>% select(tt_video_id,idea,skills_required) %>% 
  group_by(tt_video_id,idea) %>% 
  arrange(tt_video_id, skills_required) %>% 
  summarize(combination = paste0(skills_required, collapse = " - "), .groups = "drop") %>% 
  count(combination) %>% 
  filter(str_detect(combination," - ")) %>%  arrange(-n) %>% slice(1:10) %>% 
  gt() %>% tab_header("TikTok Data", subtitle="Combinations of >2 skills, Top 10 most common combinations shown")

TikTok Data
Combinations of >2 skills, Top 10 most common combinations shown
combination	n
Investing - Marketing	9
Marketing - Programming - Video Editing - Web Development - Writing	4
Doing mircowork for businesses - Doing mircowork for businesses - Writing - Writing	3
Doing mircowork for businesses - Speaking	3
Image Editing - Image Editing	3
Marketing - Marketing	3
Doing mircowork for businesses - Writing	2
Image Editing - Image Editing - Image Editing - Image Editing - Investing - Investing - Investing - Investing	2
Image Editing - Image Editing - Investing - Investing - Marketing - Marketing	2
Image Editing - Image Editing - Marketing - Marketing	2

Skills and earnings

Across both platforms, the Skill with the highest mean standardized earnings (hourly) was Investing, followed by Marketing, Video Editing, and Writing.

ttskillearn <- 
tt_hourlycorrs %>% select(tt_video_id,idea,hourly_earn,skills_required) %>% group_by(tt_video_id,idea) %>% distinct() %>% 
  group_by(skills_required) %>% 
  summarise(mean_earn=mean(hourly_earn),
            median_earn=median(hourly_earn)) %>% 
  arrange(-mean_earn) 

ytskillearn <- 
yt_hourlycorrs %>% select(yt_video_id,idea,hourly_earn,skills_required) %>% group_by(yt_video_id,idea) %>% distinct() %>% 
  group_by(skills_required) %>% 
  summarise(mean_earn=mean(hourly_earn),
            median_earn=median(hourly_earn)) %>% 
  arrange(-mean_earn)

ytskillearns <- 
yt_hourlycorrs %>% select(yt_video_id,idea,hourly_earn,skills_required) %>% group_by(yt_video_id,idea) %>% distinct() %>% 
  ungroup %>%  mutate(source="YouTube")

ttskillearns <-  
tt_hourlycorrs %>% select(tt_video_id,idea,hourly_earn,skills_required) %>% group_by(tt_video_id,idea) %>% distinct() %>% 
ungroup %>% mutate(source="TikTok")

bothearn <- bind_rows(ytskillearns,ttskillearns) %>% group_by(skills_required) %>% 
  summarise(mean_earn=mean(hourly_earn),
            median_earn=median(hourly_earn)) %>% 
  arrange(-mean_earn)

gt(bothearn) %>% tab_header(title='all platforms',subtitle = "earnings by skill")

all platforms
earnings by skill
skills_required	mean_earn	median_earn
Marketing	162.62975	62.50000
Investing	144.76703	55.55556
Video Editing	140.88650	50.00000
Doing mircowork for businesses	135.10391	46.50000
Writing	131.10958	25.99250
Speaking	100.22660	25.00000
Providing Home Services	40.89468	27.96875
Image Editing	40.24922	25.00000
Programming	38.74833	40.00000
Web Development	37.03615	37.50000
Gaming	36.00000	36.00000

By platform, differences in reported earnings appear. On YouTube, Marketing and Investing report the highest mean earnings. On TikTok, Video Editing and Investing report the highest earnings.

gt(ytskillearn)%>% tab_header(title='YouTube',subtitle = "earnings by skill")

YouTube
earnings by skill
skills_required	mean_earn	median_earn
Marketing	146.43900	62.50000
Writing	138.97708	25.00000
Doing mircowork for businesses	109.94519	45.00000
Investing	107.55416	40.00000
Video Editing	80.54883	52.05250
Speaking	47.41574	20.00000
Web Development	43.12500	38.75000
Programming	42.75000	40.00000
Image Editing	36.72155	21.04167
Providing Home Services	18.69444	19.83333

gt(ttskillearn) %>% tab_header(title='TikTok',subtitle = "earnings by skill")

TikTok
earnings by skill
skills_required	mean_earn	median_earn
Video Editing	364.99786	37.5000
Speaking	241.05556	237.5000
Marketing	205.80511	50.0000
Doing mircowork for businesses	170.85579	48.0000
Investing	169.57562	100.0000
Writing	121.66858	25.9925
Image Editing	55.06547	50.0000
Providing Home Services	51.99479	45.0000
Gaming	36.00000	36.0000
Programming	33.74625	33.4925
Web Development	27.29400	21.9850

Additionally, TikTok videos report significantly higher earnings for the same skills compared with YouTube videos.

ytskearnsummary <- ytskillearn %>% mutate(source="YouTube")
ttskearnsummary <- ttskillearn %>% mutate(source="TikTok")
bind_rows(ytskearnsummary,ttskearnsummary) %>% 
  ggplot()+
  geom_bar(aes(x=fct_reorder(skills_required,mean_earn),y=mean_earn,fill=source),stat="identity",position = "dodge")+coord_flip()+labs(x='skill')

The variation and spread of earnings by skill is consistent across platforms.

bind_rows(ytskillearns,ttskillearns) %>% 
  ggplot(aes(x=fct_reorder(skills_required,hourly_earn),y=hourly_earn,color=source))+
  geom_sina()+labs(x='skill')+
  coord_flip()

More required skills did not relate with greater earnings.

# skills_per_idea_ytid <- 
# youtube_data_activities %>% select(yt_video_id,idea,skills_required) %>% 
#   group_by(yt_video_id,idea) %>%  distinct() %>% 
# summarise(n_skills=n()) %>% ungroup() %>% mutate(source="YouTube")  
# 
# skills_per_idea_ttid <- 
# tiktok_data_activities %>% select(tt_video_id,idea,skills_required) %>% 
#   group_by(tt_video_id,idea) %>%  distinct() %>% 
# summarise(n_skills=n()) %>% ungroup()%>% mutate(source="TikTok")  

skillsearntt <- left_join(skills_per_idea_tt,tt_hourly_earn)
skillsearnyt <- left_join(skills_per_idea_yt,yt_hourly_earn)
bind_rows(skillsearntt,skillsearnyt) %>% 
  ggplot(aes(x=factor(n_skills),hourly_earn))+geom_boxplot()+
  labs(x="number of skills required per money-making idea",y="standardized earnings")

Video titles

In general, the video titles vary considerably across platforms in terms of length, content and style.

tiktok_data <-  tiktok_data %>% mutate(title_noHash=str_extract(text,"^[^#]*")) 
yt_tlength <- round(mean(str_length(youtube_data$title)),0)
tt_tlength <-round(mean(str_length(tiktok_data$text)))
tt_tlength_nh <-round(mean(str_length(tiktok_data$title_noHash)))

Without various trailing hashtags, YouTube video titles are on average, twice as long as TikTok titles (65 vs. 31 characters). Overall, roughly a third of the length of TikTok titles comprises various hashtags.

The words and bigrams (consecutive sequences of two words) that appear most frequently in the video’s titles vary significantly between platforms.

# tokenize 
stopwords <- c("for","in","a","the","to","with","from","by")
title_words_yt <- youtube_data %>% unnest_tokens(title_wrd,title,token = "words") %>% 
  filter(!title_wrd %in% stopwords)
title_bigrams_yt <- youtube_data %>% unnest_tokens(title_bg,title,token = "ngrams",n=2) 
title_words_tt <- tiktok_data %>% unnest_tokens(title_wrd,text,token = "tweets") %>% 
  filter(!title_wrd %in% stopwords)
title_bigrams_tt <- title_words_tt %>% mutate(nextwrdbg=lead(title_wrd)) %>% 
  unite(title_bg, title_wrd, nextwrdbg, sep = ' ')


wordsyt <- title_words_yt %>% count(title_wrd) %>% slice_max(n,n=15) %>% mutate(source="YouTube")
wordstt <- title_words_tt %>% count(title_wrd) %>% slice_max(n,n=15) %>% mutate(source="TikTok")
bg_yt <- title_bigrams_yt %>% count(title_bg) %>% slice_max(n,n=15) %>% mutate(source="YouTube")
bg_tt <- title_bigrams_tt %>% count(title_bg) %>% slice_max(n,n=15) %>% mutate(source="TikTok")
top15wrds <- bind_rows(wordsyt,wordstt)
top15bg <- bind_rows(bg_yt,bg_tt)

ggplot(top15wrds)+
  geom_lollipop(aes(x=fct_reorder(title_wrd,n),y=n))+
  facet_wrap(~source)+labs(x="word or hashtag",y='occurrences')+
  coord_flip()

ggplot(top15bg)+
  geom_lollipop(aes(x=fct_reorder(title_bg,n),y=n))+
  facet_wrap(~source)+labs(x="bigram",y='occurrences')+
  coord_flip()

Considering the top 15 words or bigrams, there is little overlap between platforms.